Current Issue

2020 Vol. 9, No. 3
Reviews
Over time, Synthetic Aperture Radar (SAR) imaging techniques have been developed from two-dimensional SAR, two-and-a-half-dimensional SAR (InSAR), and three-dimensional SAR to multi-dimensional SAR. This has led to great technological achievements. This paper briefly summarizes the development of SAR and its imaging technology, presents the concept of holographic SAR and clearly defines it for the first time, and highlights the differences and connection between the holographic SAR definition and existing concepts such as holographic radar, circular tomographic SAR, and multi-dimensional SAR. On this basis, under the framework of the existing multi-dimensional SAR techniques, the imaging system and signal model of holographic SAR are established, and preliminary imaging ideas are proposed, which provides a preliminary theoretical and technical framework for the development of holographic SAR technology. Over time, Synthetic Aperture Radar (SAR) imaging techniques have been developed from two-dimensional SAR, two-and-a-half-dimensional SAR (InSAR), and three-dimensional SAR to multi-dimensional SAR. This has led to great technological achievements. This paper briefly summarizes the development of SAR and its imaging technology, presents the concept of holographic SAR and clearly defines it for the first time, and highlights the differences and connection between the holographic SAR definition and existing concepts such as holographic radar, circular tomographic SAR, and multi-dimensional SAR. On this basis, under the framework of the existing multi-dimensional SAR techniques, the imaging system and signal model of holographic SAR are established, and preliminary imaging ideas are proposed, which provides a preliminary theoretical and technical framework for the development of holographic SAR technology.
The dynamic monitoring of the geological environment in urban areas, including the monitoring of the urban surface stability and detailed monitoring of man-made objects on the surface, is very important for ensuring effective and safe urban development. Spaceborne time-series InSAR technology is widely used to monitor urban deformation due to its large scale, high accuracy, and ability to acquire high-density spatial deformations. In recent years, with the operation of high-resolution satellite missions, time-series InSAR has also been widely used to monitor infrastructures. In this paper based on our long-term monitoring research experience in urban areas using the time-series InSAR technique, we review the application of some typical time-series-InSAR cases to the urban environment, including the monitoring of urban surface displacement and typical large infrastructures, including the airports, elevated road networks, bridges, railways, and subways. Based on various datasets including high-resolution TerraSAR-X images, Cosmo-SkyMed images, and recent Sentinel-1 images obtained at no cost, and the research problems and corresponding solutions identified in published monitoring research, we found good results to have been achieved using this application. With the implementation of more and more satellite missions, this technology will provide more possibilities for urban monitoring. The dynamic monitoring of the geological environment in urban areas, including the monitoring of the urban surface stability and detailed monitoring of man-made objects on the surface, is very important for ensuring effective and safe urban development. Spaceborne time-series InSAR technology is widely used to monitor urban deformation due to its large scale, high accuracy, and ability to acquire high-density spatial deformations. In recent years, with the operation of high-resolution satellite missions, time-series InSAR has also been widely used to monitor infrastructures. In this paper based on our long-term monitoring research experience in urban areas using the time-series InSAR technique, we review the application of some typical time-series-InSAR cases to the urban environment, including the monitoring of urban surface displacement and typical large infrastructures, including the airports, elevated road networks, bridges, railways, and subways. Based on various datasets including high-resolution TerraSAR-X images, Cosmo-SkyMed images, and recent Sentinel-1 images obtained at no cost, and the research problems and corresponding solutions identified in published monitoring research, we found good results to have been achieved using this application. With the implementation of more and more satellite missions, this technology will provide more possibilities for urban monitoring.
Spaceborne Synthetic Aperture Radar (SAR) can observe the ocean surface with high spatial resolution and wide swath under all-weather conditions, day and night. Thus, it is a crucial microwave sensor for obtaining information on sea surface wind and wave fields. This paper reviews various geophysical model functions for wind and wave retrieval and SAR applications in studies of marine atmospheric boundary layer phenomena, offshore wind energy resource development, typhoon monitoring/forecast. The use of traditional SAR and new types of interferometric and polarized SAR data in ocean research are discussed. With the advance of radar satellite technology, the constellation of SAR satellites has become a new trend in the global ocean observations. Many SAR research algorithms have become mature enough to be implemented operationally to provide sea surface wind and wave fields to the scientific communities for ocean dynamic environment monitoring. Spaceborne Synthetic Aperture Radar (SAR) can observe the ocean surface with high spatial resolution and wide swath under all-weather conditions, day and night. Thus, it is a crucial microwave sensor for obtaining information on sea surface wind and wave fields. This paper reviews various geophysical model functions for wind and wave retrieval and SAR applications in studies of marine atmospheric boundary layer phenomena, offshore wind energy resource development, typhoon monitoring/forecast. The use of traditional SAR and new types of interferometric and polarized SAR data in ocean research are discussed. With the advance of radar satellite technology, the constellation of SAR satellites has become a new trend in the global ocean observations. Many SAR research algorithms have become mature enough to be implemented operationally to provide sea surface wind and wave fields to the scientific communities for ocean dynamic environment monitoring.
Active radar remote sensing technology, with its capability of acquiring all-weather data, has great potential for agricultural monitoring. This technology can penetrate vegetation cover more deeply than optical sensors and has sensitivity to the shapes, structures, and dielectric constants of vegetation scatterers. In this paper, we discuss the applications of radar remote sensing in crop identification, cropland soil moisture inversion, crop growth parameter inversion, crop phenology retrieval, agricultural disaster monitoring, and crop yield estimation. We review several specific papers focusing these fields, and then describe the results obtained using information extracted from radar scatterometers and Synthetic Aperture Radar (SAR). Extracted SAR data include characterizations of backscattering, polarimetry, interferometry, and tomography. Lastly, we summarize the problems faced by radar applications in agriculture and consider the future trend of these applications. Active radar remote sensing technology, with its capability of acquiring all-weather data, has great potential for agricultural monitoring. This technology can penetrate vegetation cover more deeply than optical sensors and has sensitivity to the shapes, structures, and dielectric constants of vegetation scatterers. In this paper, we discuss the applications of radar remote sensing in crop identification, cropland soil moisture inversion, crop growth parameter inversion, crop phenology retrieval, agricultural disaster monitoring, and crop yield estimation. We review several specific papers focusing these fields, and then describe the results obtained using information extracted from radar scatterometers and Synthetic Aperture Radar (SAR). Extracted SAR data include characterizations of backscattering, polarimetry, interferometry, and tomography. Lastly, we summarize the problems faced by radar applications in agriculture and consider the future trend of these applications.
SAR Automatic Target Recognition (ATR) is a key task in microwave remote sensing. Recently, Deep Neural Networks (DNNs) have shown promising results in SAR ATR. However, despite the success of DNNs, their underlying reasoning and decision mechanisms operate essentially like a black box and are unknown to users. This lack of transparency and explainability in SAR ATR pose a severe security risk and reduce the users’ trust in and the verifiability of the decision-making process. To address these challenges, in this paper, we argue that research on the explainability and interpretability of SAR ATR is necessary to enable development of interpretable SAR ATR models and algorithms, and thereby, improve the validity and transparency of AI-based SAR ATR systems. First, we present recent developments in SAR ATR, note current practical challenges, and make a plea for research to improve the explainability and interpretability of SAR ATR. Second, we review and summarize recent research in and practical applications of explainable machine learning and deep learning. Further, we discuss aspects of explainable SAR ATR with respect to model understanding, model diagnosis, and model improvement toward a better understanding of the internal representations and decision mechanisms. Moreover, we emphasize the need to exploit interpretable SAR feature learning and recognition models that integrate SAR physical characteristics and domain knowledge. Finally, we draw our conclusion and suggest future work for SAR ATR that combines data and knowledge-driven methods, human–computer cooperation, and interactive deep learning. SAR Automatic Target Recognition (ATR) is a key task in microwave remote sensing. Recently, Deep Neural Networks (DNNs) have shown promising results in SAR ATR. However, despite the success of DNNs, their underlying reasoning and decision mechanisms operate essentially like a black box and are unknown to users. This lack of transparency and explainability in SAR ATR pose a severe security risk and reduce the users’ trust in and the verifiability of the decision-making process. To address these challenges, in this paper, we argue that research on the explainability and interpretability of SAR ATR is necessary to enable development of interpretable SAR ATR models and algorithms, and thereby, improve the validity and transparency of AI-based SAR ATR systems. First, we present recent developments in SAR ATR, note current practical challenges, and make a plea for research to improve the explainability and interpretability of SAR ATR. Second, we review and summarize recent research in and practical applications of explainable machine learning and deep learning. Further, we discuss aspects of explainable SAR ATR with respect to model understanding, model diagnosis, and model improvement toward a better understanding of the internal representations and decision mechanisms. Moreover, we emphasize the need to exploit interpretable SAR feature learning and recognition models that integrate SAR physical characteristics and domain knowledge. Finally, we draw our conclusion and suggest future work for SAR ATR that combines data and knowledge-driven methods, human–computer cooperation, and interactive deep learning.
Synthetic Aperture Radar (SAR), which features rich imaging modes, wide coverage, and high resolution, is an effective technique for long-term, dynamic, and large-scale monitoring of the ocean. Under the assumption of fully developed speckle, traditional ship detection methods in single-channel SAR images focus mainly on amplitude information. Since conventional assumptions are not strictly true in high-resolution situations, this prevents the full investigation of phase or complex-valued information in single-channel SAR images. In this paper, with a focus on ship detection applications, we categories the methods used in the statistical modeling of single-channel complex-valued SAR images as amplitude-, phase-, or complex-valued-based. After providing a brief overview of amplitude statistical modeling methods, we focus on phase and complex-valued statistical modeling methods of single-channel SAR images, describing their modeling processes and parameter estimation methods. We then present the results of our recent ship detection research based on complex-valued statistical information in single-channel SAR images and make suggestions regarding future research. Synthetic Aperture Radar (SAR), which features rich imaging modes, wide coverage, and high resolution, is an effective technique for long-term, dynamic, and large-scale monitoring of the ocean. Under the assumption of fully developed speckle, traditional ship detection methods in single-channel SAR images focus mainly on amplitude information. Since conventional assumptions are not strictly true in high-resolution situations, this prevents the full investigation of phase or complex-valued information in single-channel SAR images. In this paper, with a focus on ship detection applications, we categories the methods used in the statistical modeling of single-channel complex-valued SAR images as amplitude-, phase-, or complex-valued-based. After providing a brief overview of amplitude statistical modeling methods, we focus on phase and complex-valued statistical modeling methods of single-channel SAR images, describing their modeling processes and parameter estimation methods. We then present the results of our recent ship detection research based on complex-valued statistical information in single-channel SAR images and make suggestions regarding future research.
Target detection and recognition are popular issues in the field of high-resolution Synthetic Aperture Radar (SAR). As a typical target, aircraft detection and identification has certain uniqueness. This paper reviews the development of detection and recognition techniques for a typical target in SAR imagery, analyzes the scattering mechanism and technical difficulties of aircraft in SAR imagery, describes the system flow, technical routes, and key scientific problems of target aircraft detection and recognition in SAR imagery, summarizes the research progress from traditional methods to deep-learning-based methods for aircraft detection and recognition, discusses the characteristics and existing problems of various methods, and predicts the future development trend. This paper proposes that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance. Moreover, this paper establishes an aircraft detection method based on the fusion of scattering information and deep convolutional neural network. Target detection and recognition are popular issues in the field of high-resolution Synthetic Aperture Radar (SAR). As a typical target, aircraft detection and identification has certain uniqueness. This paper reviews the development of detection and recognition techniques for a typical target in SAR imagery, analyzes the scattering mechanism and technical difficulties of aircraft in SAR imagery, describes the system flow, technical routes, and key scientific problems of target aircraft detection and recognition in SAR imagery, summarizes the research progress from traditional methods to deep-learning-based methods for aircraft detection and recognition, discusses the characteristics and existing problems of various methods, and predicts the future development trend. This paper proposes that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance. Moreover, this paper establishes an aircraft detection method based on the fusion of scattering information and deep convolutional neural network.
Papers
Ground-based radar is a microwave remote sensing imaging technology that has been gradually developed throughout the past 20 years so that it has become mature. At present, it has been widely used in monitoring geological disasters such as landslides and collapses. Ground-based radars can detect micro-variations in target areas through the principle of interferometry. However, due to human factors, geological factors, and meteorological factors, the radar image of the monitored area is incoherent, which makes long-term quantitative monitoring difficult. Therefore, further developing the application of change detection while considering quantitative monitoring is urgent, to provide effective information on long-term changes and comprehensively understand the dynamic changes in the monitored area. To solve the above problems, an unsupervised change detection method using ground-based radar images and based on an improved Fuzzy C-Means clustering (FCM) algorithm is proposed in this paper. In this method, for the first time, the Nonsubsampled Contourlet Transform (NSCT) is performed on the coherence coefficient map and the mean log ratio map to obtain the fusion difference map. Then, principal component analysis is used to extract the feature vectors of each pixel in the fusion difference image. The FCM is improved according to the characteristics of the ground-based radar images. The improved FCM is used to cluster the feature vectors of each pixel to obtain the change detection result. A ground-based radar LSA was used to monitor the treatment process of a dam in southwest China. During the monitoring process, landslides occurred in the monitored area affected by precipitation and other factors. This method is used to detect the change of the radar image before and after the landslide. The results show that the proposed method allows for easier clustering and segmenting, and the change detection results can significantly reduce the noise points while retaining the change area. Ground-based radar is a microwave remote sensing imaging technology that has been gradually developed throughout the past 20 years so that it has become mature. At present, it has been widely used in monitoring geological disasters such as landslides and collapses. Ground-based radars can detect micro-variations in target areas through the principle of interferometry. However, due to human factors, geological factors, and meteorological factors, the radar image of the monitored area is incoherent, which makes long-term quantitative monitoring difficult. Therefore, further developing the application of change detection while considering quantitative monitoring is urgent, to provide effective information on long-term changes and comprehensively understand the dynamic changes in the monitored area. To solve the above problems, an unsupervised change detection method using ground-based radar images and based on an improved Fuzzy C-Means clustering (FCM) algorithm is proposed in this paper. In this method, for the first time, the Nonsubsampled Contourlet Transform (NSCT) is performed on the coherence coefficient map and the mean log ratio map to obtain the fusion difference map. Then, principal component analysis is used to extract the feature vectors of each pixel in the fusion difference image. The FCM is improved according to the characteristics of the ground-based radar images. The improved FCM is used to cluster the feature vectors of each pixel to obtain the change detection result. A ground-based radar LSA was used to monitor the treatment process of a dam in southwest China. During the monitoring process, landslides occurred in the monitored area affected by precipitation and other factors. This method is used to detect the change of the radar image before and after the landslide. The results show that the proposed method allows for easier clustering and segmenting, and the change detection results can significantly reduce the noise points while retaining the change area.
In this study, a weakly supervised classification method is proposed to classify the Polarimetric Synthetic Aperture Radar (PolSAR) images based on sample refinement using a Complex-Valued Convolutional Neural Network (CV-CNN) to solve the problem that the bounding-box labeled samples contain many heterogeneous components. First, CV-CNN is used for iteratively refining the bounding-box labeled samples, and the CV-CNN that can be used for direct classification is trained simultaneously. Then, the given PolSAR image is classified using the trained CV-CNN. The experimental results obtained using three actual PolSAR images demonstrate that the heterogeneous components can be effectively eliminated using the proposed method, obtaining significantly better classification results when compared with those obtained using the traditional fully supervised classification method in which original bounding-box labeled samples are used. Furthermore, the proposed method with CV-CNN is superior to those in which the classical Support Vector Machine(SVM) and Wishart classifier are used. In this study, a weakly supervised classification method is proposed to classify the Polarimetric Synthetic Aperture Radar (PolSAR) images based on sample refinement using a Complex-Valued Convolutional Neural Network (CV-CNN) to solve the problem that the bounding-box labeled samples contain many heterogeneous components. First, CV-CNN is used for iteratively refining the bounding-box labeled samples, and the CV-CNN that can be used for direct classification is trained simultaneously. Then, the given PolSAR image is classified using the trained CV-CNN. The experimental results obtained using three actual PolSAR images demonstrate that the heterogeneous components can be effectively eliminated using the proposed method, obtaining significantly better classification results when compared with those obtained using the traditional fully supervised classification method in which original bounding-box labeled samples are used. Furthermore, the proposed method with CV-CNN is superior to those in which the classical Support Vector Machine(SVM) and Wishart classifier are used.
The flooded area detection method based on the fusion of optical and Synthetic Aperture Radar (SAR) images is applicable for all weather conditions and times. However, due to the large number of randomly distributed intensive speckle noise in SAR images, the conventional methods of detection often trigger high false alarm rates at flood-stricken zones. Inspired by the Fuzzy C-Means (FCM) clustering method, a hierarchical clustering algorithm (Hierarchical Fuzzy C-Means, H-FCM) is proposed in this paper. This method fuses the SAR image captured after the flood with the optical image captured before the flood. Based on the fused image, this method uses the proposed hierarchical clustering model to obtain the preliminary detection results of the flooded area. Additionally, the algorithm uses the proposed region-growing algorithm to obtain the river location before the flood and uses it as a spatial constraint for the preliminary detection results to further screen out suspected flooded areas and significantly improve detection performance. The experimental data used in this paper include the remote sensing images captured before and after the Gloucester floods in the United Kingdom in 1999, as well as the remote sensing images captured before and after the Nanchang floods in China in 2019. The effectiveness and validity of the H-FCM algorithm are also supported by comparison experiments. The flooded area detection method based on the fusion of optical and Synthetic Aperture Radar (SAR) images is applicable for all weather conditions and times. However, due to the large number of randomly distributed intensive speckle noise in SAR images, the conventional methods of detection often trigger high false alarm rates at flood-stricken zones. Inspired by the Fuzzy C-Means (FCM) clustering method, a hierarchical clustering algorithm (Hierarchical Fuzzy C-Means, H-FCM) is proposed in this paper. This method fuses the SAR image captured after the flood with the optical image captured before the flood. Based on the fused image, this method uses the proposed hierarchical clustering model to obtain the preliminary detection results of the flooded area. Additionally, the algorithm uses the proposed region-growing algorithm to obtain the river location before the flood and uses it as a spatial constraint for the preliminary detection results to further screen out suspected flooded areas and significantly improve detection performance. The experimental data used in this paper include the remote sensing images captured before and after the Gloucester floods in the United Kingdom in 1999, as well as the remote sensing images captured before and after the Nanchang floods in China in 2019. The effectiveness and validity of the H-FCM algorithm are also supported by comparison experiments.
Landslide disasters occur frequently in the western mountainous regions of China and are characterized by high concealment, suddenness, and strong destructiveness. Early identification of potential disaster hazards is the most effective disaster prevention and mitigation measure. The western mountainous areas are mostly in alpine-canyon terrain with a wide range, which is hard or even impossible to reach, where the traditional early identification methods of manual inspection are difficult to implement. Interferometric Synthetic Aperture Radar (InSAR), as an emerging radar remote-sensing method, can efficiently and accurately identify the hidden dangers of landslide disasters. In this study, based on the European Space Agency’s Sentinel-1 synthetic aperture radar data, the time series InSAR technology was used to identify the potential landslide hazards in alpine-canyon terrain along Yajiang County-Muli County of the Yalong River, detecting eight potential geohazards. Based on the historical data of landslide hazards and optical remote sensing interpretation, the early identification results were verified and analyzed, and the danger level of the disaster points was evaluated. The influence of geometric distortion in InSAR technology on the early identification of potential landslide disasters in alpine-canyon terrain is also discussed. This case can provide powerful data and technical support for local disaster prevention and mitigation and provide ideas and references for the early identification of the hidden dangers of landslide disasters in mountain-valley areas. Landslide disasters occur frequently in the western mountainous regions of China and are characterized by high concealment, suddenness, and strong destructiveness. Early identification of potential disaster hazards is the most effective disaster prevention and mitigation measure. The western mountainous areas are mostly in alpine-canyon terrain with a wide range, which is hard or even impossible to reach, where the traditional early identification methods of manual inspection are difficult to implement. Interferometric Synthetic Aperture Radar (InSAR), as an emerging radar remote-sensing method, can efficiently and accurately identify the hidden dangers of landslide disasters. In this study, based on the European Space Agency’s Sentinel-1 synthetic aperture radar data, the time series InSAR technology was used to identify the potential landslide hazards in alpine-canyon terrain along Yajiang County-Muli County of the Yalong River, detecting eight potential geohazards. Based on the historical data of landslide hazards and optical remote sensing interpretation, the early identification results were verified and analyzed, and the danger level of the disaster points was evaluated. The influence of geometric distortion in InSAR technology on the early identification of potential landslide disasters in alpine-canyon terrain is also discussed. This case can provide powerful data and technical support for local disaster prevention and mitigation and provide ideas and references for the early identification of the hidden dangers of landslide disasters in mountain-valley areas.
To compensate for the limitations of insufficient observation information and simplistic geometric structure of single-baseline InSAR, this study proposes a new method for extracting forest height from ALOS-2 PARSAR-2 multi-baseline PolInSAR datas. Firstly, the Maximum Coherence Difference (MCD) algorithm is introduced to determine the polarization; this algorithm is very sensitive to volume scattering in the polarization space. Then, with the aid of a small amount of externally known forest height data, the coherence amplitude of the polarization is used to solve the temporal decorrelation semi-empirical scattering model. In addition, multi-baseline datas are further fused to increase the diversity of observation geometry and improve the reliability of the inversion results. To verify the effectiveness of the proposed method, we selected Huangfengqiao Forestry Center in Hunan, China as the study area and used three pairs of ALOS-2 PALSAR-2 interferometric images with 14-day temporal baseline for the experimental analysis. The experimental results showed that the method proposed in this study effectively improved the assumptions and addressed the limitation of the existing method that is only applicable to single-baseline interferometric data. Thus, the inversion accuracy can be improved by at least 40%. To compensate for the limitations of insufficient observation information and simplistic geometric structure of single-baseline InSAR, this study proposes a new method for extracting forest height from ALOS-2 PARSAR-2 multi-baseline PolInSAR datas. Firstly, the Maximum Coherence Difference (MCD) algorithm is introduced to determine the polarization; this algorithm is very sensitive to volume scattering in the polarization space. Then, with the aid of a small amount of externally known forest height data, the coherence amplitude of the polarization is used to solve the temporal decorrelation semi-empirical scattering model. In addition, multi-baseline datas are further fused to increase the diversity of observation geometry and improve the reliability of the inversion results. To verify the effectiveness of the proposed method, we selected Huangfengqiao Forestry Center in Hunan, China as the study area and used three pairs of ALOS-2 PALSAR-2 interferometric images with 14-day temporal baseline for the experimental analysis. The experimental results showed that the method proposed in this study effectively improved the assumptions and addressed the limitation of the existing method that is only applicable to single-baseline interferometric data. Thus, the inversion accuracy can be improved by at least 40%.
Building damage assessment is important in disaster emergency monitoring. In recent years, with the increase of multi-polarization capability of Synthetic Aperture Radar (SAR), Polarimetric Synthetic Aperture Radar (PolSAR) provides more possibilities for building damage assessment, and the polarization-characteristic-based building damage assessment method has gradually become the focus of research. However, because of the limitations of data acquisition in PolSAR, current research mainly focuses on the L, C, X, and other limited bands. To obtain an in depth understanding of the polarization characteristics of damaged buildings in SAR images and develop the application of the polarization characteristics of damaged buildings to other bands, this study conducted a simulation experiment of Ku band polarized SAR of buildings, and performed damage assessment feature analysis using the SAR image polarization decomposition method. In this study, a scale model of real materials was built and the “microwave characteristic measurement and simulation imaging scientific experiment platform” was used to conduct SAR simulation imaging of the target buildings. The Ku band polarized SAR images before and after building damage were obtained. Then, the polarization scattering characteristics of buildings before and after damage were analyzed using various common polarization decomposition methods such as \begin{document}$ {H/A/\alpha} $\end{document} decomposition, Yamaguchi decomposition and Touzi decomposition. Results show that the disoriented volume scattering component and the proportion of the disoriented secondary scattering component obtained by the Yamaguchi decomposition and the \begin{document}${ {\alpha }_{\rm s1}} $\end{document} component obtained by the Touzi decomposition have good indicative significance for building damage assessment in the Ku band. Compared with the X band measurement results, the Ku band is more sensitive to building damage assessment, which has important implications for future radar remote sensing applications. Building damage assessment is important in disaster emergency monitoring. In recent years, with the increase of multi-polarization capability of Synthetic Aperture Radar (SAR), Polarimetric Synthetic Aperture Radar (PolSAR) provides more possibilities for building damage assessment, and the polarization-characteristic-based building damage assessment method has gradually become the focus of research. However, because of the limitations of data acquisition in PolSAR, current research mainly focuses on the L, C, X, and other limited bands. To obtain an in depth understanding of the polarization characteristics of damaged buildings in SAR images and develop the application of the polarization characteristics of damaged buildings to other bands, this study conducted a simulation experiment of Ku band polarized SAR of buildings, and performed damage assessment feature analysis using the SAR image polarization decomposition method. In this study, a scale model of real materials was built and the “microwave characteristic measurement and simulation imaging scientific experiment platform” was used to conduct SAR simulation imaging of the target buildings. The Ku band polarized SAR images before and after building damage were obtained. Then, the polarization scattering characteristics of buildings before and after damage were analyzed using various common polarization decomposition methods such as \begin{document}$ {H/A/\alpha} $\end{document} decomposition, Yamaguchi decomposition and Touzi decomposition. Results show that the disoriented volume scattering component and the proportion of the disoriented secondary scattering component obtained by the Yamaguchi decomposition and the \begin{document}${ {\alpha }_{\rm s1}} $\end{document} component obtained by the Touzi decomposition have good indicative significance for building damage assessment in the Ku band. Compared with the X band measurement results, the Ku band is more sensitive to building damage assessment, which has important implications for future radar remote sensing applications.